"""
Implementation of attack methods. Running this file as a program will
apply the attack to the model specified by the config file and store
the examples in an .npy file.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import numpy as np

import cifar10_input


class LinfPGDAttack:
    def __init__(self, model, epsilon, num_steps, step_size, random_start, loss_func):
        """Attack parameter initialization. The attack performs k steps of
       size a, while always staying within epsilon from the initial
       point."""
        self.model = model
        self.epsilon = epsilon
        self.num_steps = num_steps
        self.step_size = step_size
        self.rand = random_start

        if loss_func == 'xent':
            loss = model.xent
        elif loss_func == 'cw':
            label_mask = tf.one_hot(model.y_input,
                                    10,
                                    on_value=1.0,
                                    off_value=0.0,
                                    dtype=tf.float32)
            correct_logit = tf.reduce_sum(label_mask * model.pre_softmax, axis=1)
            wrong_logit = tf.reduce_max((1 - label_mask) * model.pre_softmax - 1e4 * label_mask, axis=1)
            loss = -tf.nn.relu(correct_logit - wrong_logit + 50)
        else:
            print('Unknown loss function. Defaulting to cross-entropy')
            loss = model.xent

        self.grad = tf.gradients(loss, model.x_input)[0]

    def perturb(self, x_nat, y, sess):
        """Given a set of examples (x_nat, y), returns a set of adversarial
       examples within epsilon of x_nat in l_infinity norm."""
        if self.rand:
            x = x_nat + np.random.uniform(-self.epsilon, self.epsilon, x_nat.shape)
            x = np.clip(x, 0, 255)  # ensure valid pixel range
        else:
            x = np.copy(x_nat)

        for i in range(self.num_steps):
            grad = sess.run(self.grad, feed_dict={self.model.x_input: x,
                                                  self.model.y_input: y})

            x = np.add(x, self.step_size * np.sign(grad), out=x, casting='unsafe')

            x = np.clip(x, x_nat - self.epsilon, x_nat + self.epsilon)
            x = np.clip(x, 0, 255)  # ensure valid pixel range

        return x


if __name__ == '__main__':
    import json
    import sys
    import math

    from model import Model

    with open('config.json') as config_file:
        config = json.load(config_file)

    model_file = tf.train.latest_checkpoint(config['model_dir'])
    if model_file is None:
        print('No model found')
        sys.exit()

    model = Model(mode='eval')
    attack = LinfPGDAttack(model,
                           config['epsilon'],
                           config['num_steps'],
                           config['step_size'],
                           config['random_start'],
                           config['loss_func'])
    saver = tf.train.Saver()

    data_path = config['data_path']
    cifar = cifar10_input.CIFAR10Data(data_path)

    gpu_options = tf.GPUOptions(visible_device_list='7', per_process_gpu_memory_fraction=0.5)
    tf_config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
    with tf.Session(config=tf_config) as sess:
        # Restore the checkpoint
        saver.restore(sess, model_file)

        # Iterate over the samples batch-by-batch
        num_eval_examples = config['num_eval_examples']
        eval_batch_size = config['eval_batch_size']
        num_batches = int(math.ceil(num_eval_examples / eval_batch_size))

        x_adv = []  # adv accumulator

        print('Iterating over {} batches'.format(num_batches))

        for ibatch in range(num_batches):
            bstart = ibatch * eval_batch_size
            bend = min(bstart + eval_batch_size, num_eval_examples)
            print('batch size: {}'.format(bend - bstart))

            x_batch = cifar.eval_data.xs[bstart:bend, :]
            y_batch = cifar.eval_data.ys[bstart:bend]

            x_batch_adv = attack.perturb(x_batch, y_batch, sess)

            x_adv.append(x_batch_adv)

        print('Storing examples')
        path = config['store_adv_path']
        x_adv = np.concatenate(x_adv, axis=0)
        np.save(path, x_adv)
        print('Examples stored in {}'.format(path))
